Mechanism linking recycled HDPE particle size and filament 3D printing performance based on computer simulation

Abstract With the increasing severity of global plastic pollution and the rapid development of additive manufacturing technology, how to achieve the stable utilization of recycled plastics in high-performance and high-value-added applications has become one of the key issues of concern in the field of materials engineering. Recycled high-density polyethylene has great potential in fused deposition modeling, but the mechanism by which fluctuations in its raw material properties affect molding quality and service performance still lacks a systematic understanding. However, existing research mainly concentrates on process parameter optimization and lacks systematic analysis of material characteristics, especially neglecting the direct influence of recycled particle size on printing performance. Traditional experimental methods are costly and time-consuming, making it difficult to reveal the interaction mechanism of multiple factors. Therefore, this study proposes a computer simulation-based approach to investigate the mechanism linking recycled high-density polyethylene particle size and filament 3D printing performance and establishes a quantitative correlation between particle characteristics and printing performance. Experimental results show that recycled high-density polyethylene particle size significantly affects melt homogeneity and melt rheological behavior. Particle sizes in the range of 0.5–1.0 × 10 −3 m enable sufficient melting, stable extrusion, and dense interlayer bonding, which collectively improve mechanical performance, dimensional accuracy, and thermal stability. In contrast, excessively large or widely distributed particles easily lead to uneven melting, defect formation, and performance degradation. These results indicate that the proposed mechanism systematically reveals the intrinsic relationship among particle size, process, and performance, effectively addressing the current issues of unstable printing performance and reliance on empirical trial-and-error for process parameters. This work provides new ideas for plastic recycling and contributes to the intelligent and efficient development of additive manufacturing.